idblr / ndi Goto Github PK

Compute various geospatial neighborhood deprivation indices

License: Other

R 97.92% Rez 2.08%

census census-api census-data deprivation deprivation-stats geospatial geospatial-data principal-component-analysis r r-package

ndi's Issues

NDIQuint Sorting Question with Powell-Wiley

hey @idblr! I've been doing more testing and found something curious that I'm hoping you can straighten out for me.

df <- ndi::powell_wiley(geo = "county", state = "MO", year = 2020, round_output = FALSE)[[1]]
df <- dplyr::arrange(df, NDI)

On rows 69 and 70 of the resulting df object, Barry County (29009) gets a score of 0.24683105 while Sullivan County (29211) gets a score of 0.26129508. However, in NDIQunit, Barry County is given 4-AboveAvg deprivation while Sullivan is given 3-Average deprivation.

This is possible because the log of the total population is factored into the ranking process, correct?

Krieger function not found

Hy,
While checking the codes, I am seeing the krieger function is not working. How can I resolve this issue?

Error in krieger(state = "TX", year = 2020) :
could not find function "krieger"

Thanks
Rasel

ndi version 0.1.3 reverse dependency check failure when CENSUS_API_KEY not ""

00check.log
testthat.Rout.zip

If Sys.getenv("CENSUS_API_KEY") != ""`, the tests are not skipped, and are not silent:

> nzchar(Sys.getenv("CENSUS_API_KEY"))
[1] TRUE
> anthopolos(state = "DC", year = 2020, subgroup = c("NHoLB", "HoLB"))
  |======================================================================| 100%
$ri
# A tibble: 206 × 8
   GEOID       state                county      tract     RI Total…¹ NHoLB  HoLB
   <chr>       <chr>                <chr>       <chr>  <dbl>   <dbl> <dbl> <dbl>
 1 11001000101 District of Columbia District o… 1.01  0.0390    1250     0     0
 2 11001000102 District of Columbia District o… 1.02  0.0413    3318    34     0
 3 11001000201 District of Columbia District o… 2.01  0.0457    3972   239     8
 4 11001000202 District of Columbia District o… 2.02  0.0371    4665   131    11
 5 11001000300 District of Columbia District o… 3     0.0536    6504   178     0
 6 11001000400 District of Columbia District o… 4     0.0495    1481    32     0
 7 11001000501 District of Columbia District o… 5.01  0.101     3343   233     0
 8 11001000502 District of Columbia District o… 5.02  0.0616    3580   150    20
 9 11001000600 District of Columbia District o… 6     0.0749    4942   411     0
10 11001000702 District of Columbia District o… 7.02  0.0763    2971   335     0
# … with 196 more rows, and abbreviated variable name ¹TotalPop
# ℹ Use `print(n = ...)` to see more rows

$missing
# A tibble: 3 × 4
  variable total n_missing percent_missing
  <chr>    <int>     <int> <chr>          
1 HoLB       206         0 0 %            
2 NHoLB      206         0 0 %            
3 TotalPop   206         0 0 %            

> bravo(state = "DC", year = 2009, subgroup = c("LtHS", "HSGiE"))
  |======================================================================| 100%
$ei
# A tibble: 188 × 24
   GEOID   state county tract     EI Total…¹  mNSC mNt4G m5t6G m7t8G   m9G  m10G
   <chr>   <chr> <chr>  <chr>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 110010… Dist… Distr… 1     0.0524    3882     0     0     0     0     0     0
 2 110010… Dist… Distr… 2.01  0.0522     127     0     0     0     0     0     0
 3 110010… Dist… Distr… 2.02  0.0442    2371     0     0     0     0     0     0
 4 110010… Dist… Distr… 3     0.0732    3563     0     0     0     0     0     0
 5 110010… Dist… Distr… 4     0.0832    1099     0     0     0     0     0     0
 6 110010… Dist… Distr… 5.01  0.0809    2426     0     0     0     0     0     0
 7 110010… Dist… Distr… 5.02  0.0942    2471     0     0     7     0     0     0
 8 110010… Dist… Distr… 6     0.104     4436    10     0     8    37     0   146
 9 110010… Dist… Distr… 7.01  0.114     3782     0     0     0     0    26     0
10 110010… Dist… Distr… 7.02  0.0805    2237     0     0     0    33     0     0
# … with 178 more rows, 12 more variables: m11G <dbl>, m12GND <dbl>,
#   mHSGGEDoA <dbl>, fNSC <dbl>, fNt4G <dbl>, f5t6G <dbl>, f7t8G <dbl>,
#   f9G <dbl>, f10G <dbl>, f11G <dbl>, f12GND <dbl>, fHSGGEDoA <dbl>, and
#   abbreviated variable name ¹TotalPop
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

$missing
# A tibble: 19 × 4
   variable  total n_missing percent_missing
   <chr>     <int>     <int> <chr>          
 1 f10G        188         0 0 %            
 2 f11G        188         0 0 %            
 3 f12GND      188         0 0 %            
 4 f5t6G       188         0 0 %            
 5 f7t8G       188         0 0 %            
 6 f9G         188         0 0 %            
 7 fHSGGEDoA   188         0 0 %            
 8 fNSC        188         0 0 %            
 9 fNt4G       188         0 0 %            
10 m10G        188         0 0 %            
11 m11G        188         0 0 %            
12 m12GND      188         0 0 %            
13 m5t6G       188         0 0 %            
14 m7t8G       188         0 0 %            
15 m9G         188         0 0 %            
16 mHSGGEDoA   188         0 0 %            
17 mNSC        188         0 0 %            
18 mNt4G       188         0 0 %            
19 TotalPop    188         0 0 %

Refactoring to Allow for Existing Data

Great work on ndi, @idblr!

I'd love to integrate your package into some existing workflows, but would like to be able to pass a data frame of correctly prepped/formatted data to messer() or one of the other functions.

The sociome package has a great workaround that allows this, the calculate_adi() function. Instead of calling the main get_adi() function that downloads the data and calculates ADI, users with pre-existing data can skip the download step by calling calculate_adi() directly.

I'm wondering if you'd be open to a PR that would create (as an example) a calculate_messer() function that messer() would call as a subfunction after data download and prep. I would also export it so that other workflows could call it directly. I'm most interested in this for messer() and powell_wiley(), but would happily write it for anthopolos(), bravo(), and krieger() as well (doesn't make sense to do it for gini()). Thanks for considering!

Significant Digits

One additional question that popped up in messer() is the rounding that takes place at the end. I'm sure you had a reason for doing it! Just curious your thoughts about whether that should be optional?

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